Multimodal Evidence-Based License Compliance Verification for KOGL Open Government Works on the Web
DOI:
https://doi.org/10.13052/jwe1540-9589.2576Keywords:
Web Engineering, KOGL, Open Government Works, License Compliance, Copyright Risk PredictionAbstract
The reuse of open government works on the web has increased rapidly with the expansion of public data policies and artificial intelligence (AI)-assisted content utilization. In Korea, Korea Open Government Licenses (KOGLs) permit the reuse of public works under conditions such as attribution, non-commercial use, and no-derivatives. However, these conditions are frequently violated or misunderstood in web pages, advertisements, social media posts, and reused digital content. Since compliance-related evidence is distributed across textual descriptions, captions, license metadata, embedded image text, visual changes, and page-level context, manual inspection, and single-modal analysis are not sufficient for scalable web-based compliance review.
This paper proposes a multimodal evidence-based method for KOGL license compliance risk prediction in web environments. The proposed method combines textual context, KOGL metadata, optical character recognition (OCR) extracted image text, and visual features to classify four risk types: attribution risk, non-commercial use risk, no-derivatives risk, and false KOGL claim risk. A lightweight sequential context model captures the contextual flow of compliance-related cues, while the decision module produces both risk probabilities and evidence-based explanations. Experiments on a KOGL-oriented dataset show that the proposed method achieves higher accuracy, macro-F1, and area under the curve (AUC) than text-only, image-only, and simple fusion baselines. The results demonstrate that multimodal web evidence improves compliance risk prediction and provides practical support for human review in public content management, open government data curation, and AI training data inspection.
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